Abstract
Recent studies on image restoration (IR) methods under unrolled optimization frameworks have shown that deep convolutional neural networks (DCNNs) can be implicitly used as priors to solve inverse problems. Due to the ill-conditioned nature of the inverse problem, the selection of prior knowledge is crucial for the process of IR. However, the existing methods use a fixed DCNN in each iteration, and so they cannot fully adapt to the image characteristics at each iteration stage. In this paper, we combine deep learning with traditional optimization and propose an end-to-end unrolled network based on deep priors. The entire network contains several iterations, and each iteration is composed of analytic solution updates and a small multiscale deep denoiser network. In particular, we use different denoiser networks at different stages to improve adaptability. Compared with a fixed DCNN, it greatly reduces the number of computations when the total parameters are equal and the number of iterations is the same, but the gains from a practical runtime are not as significant as indicated in the FLOP count. The experimental results of our method of three IR tasks, including denoising, deblurring, and lensless imaging, demonstrate that our proposed method achieves state-of-the-art performances in terms of both visual effects and quantitative evaluations.
Highlights
Image restoration (IR) is a classical topic in the field of low-level image processing.Digital images are always degraded during the acquisition process, with issues such as electronic noise caused by the thermal vibration of atoms and blur caused by camera shake [1,2]
Compared with a fixed deep convolutional neural networks (DCNNs), it greatly reduces the number of computations when the total parameters are the same and the number of iterations is the same
The unrolled network we proposed is mainly derived from two aspects: deep learning and denoiser-based IR methods under unrolled optimization
Summary
Image restoration (IR) is a classical topic in the field of low-level image processing. Model-based methods can deal with all kinds of visual problems flexibly, but they usually incur high time and computational costs, and their effects are not as good as those of the popular DCNNs. With the rapid development of deep learning, DCNNs have blossomed in the field of low-level vision. Learning-based DCNNs can quickly complete high-quality image reconstruction on GPUs after training, they usually lose the flexibility inherent in model-based methods. When solving the inverse problem, they can incorporate the physical models of systems into the networks This structure can make full use of the prior knowledge of the systems, such as the observation matrix in compressed sensing and the point spread function in deconvolution.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
